Deep Feature Extraction for Resting-State Functional MRI by Self-Supervised Learning and Application to Schizophrenia Diagnosis
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Abstract
In this study, we propose a deep-learning technique for functional MRI analysis. We introduced a novel self-supervised learning scheme that is particularly useful for functional MRI wherein the subject identity is used as the teacher signal of a neural network. The neural network is trained solely based on functional MRI-scans, and the training does not require any explicit labels. The proposed method demonstrated that each temporal volume of resting state functional MRI contains enough information to identify the subject. The network learned a feature space in which the features were clustered per subject for the test data as well as for the training data; this is unlike the features extracted by conventional methods including region of interests (ROIs) pooling signals and principal component analysis. In addition, applying a simple linear classifier to the per-subject mean of the features (namely “identity feature”), we demonstrated that the extracted features could contribute to schizophrenia diagnosis. The classification accuracy of our identity features was comparable to that of the conventional functional connectivity. Our results suggested that our proposed training scheme of the neural network captured brain functioning related to the diagnosis of psychiatric disorders as well as the identity of the subject. Our results together highlight the validity of our proposed technique as a design for self-supervised learning.
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A particularly interesting medical task explored was classification of psychiatric diseases or psychiatric traits using fMRI.
“A particularly interesting medical task that was explored was classification of psychiatric diseases or psychiatric traits using fMRI57,62,90.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [90]
Osin et al. and Hashimoto demonstrated that training a self-supervised framework could be beneficial to generate representative latent features of brain fMRIs before fine-tuning on image-level class labels.
“In this case both Osin et al. and Hashimoto demonstrated that training a self-supervised framework could be beneficial to generate representative latent features of brain fMRIs before fine-tuning on image-level class labels57,90.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [90]
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“A particularly interesting medical task that was explored was classi fi cation of psychiatric diseases or psychiatric traits using fMRI 57,62,90 .”
BackgroundMethodChen, Z., Liu, X., Yang, Q., Wang, Y.-J., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.1671 · Cited by 43 · see citing works
- Authors
- Hashimoto, Y., Ogata, Y., Honda, M., & Yamashita, Y.
- Affiliations
- National Center of Neurology and Psychiatry (JP)
- Tokyo Institute of Technology (JP)
- Corresponding author
- Yamashita, Yuichi
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2021-08-27
- Volume
- 15
- Pages
- 696853-696853
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC8429808
- OpenAlex
- W3197152041
- Cited by
- 19
- References
- 21
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 1
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Japan Society for the Promotion of Science (JP17H06039, JP18KT0021, JP18K07597, JP19H04998, JP20H00625)
- Japan Science and Technology Agency (JPMJCR16E2)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- resting-state fMRI, self-supervised learning, deep feature extraction, schizophrenia diagnosis, linear classification, brain-computer interface, functional connectivity, mental illness
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